Dalal Khalid Almutairi

Background: Automated interpretation of 12-lead electrocardiograms (ECGs) remains challenging because multiple abnormalities may coexist and appear in selected leads or brief waveform segments. We developed a compact and interpretable framework for five-superclass multi-label ECG diagnosis. Methods: We evaluated PTB-XL records using the official fold protocol, with folds 1–8 for training, fold 9 for validation monitoring and class-specific threshold selection, and fold 10 for independent internal testing. We then evaluated the frozen 1.33-million-parameter InceptionTime–CNN–BiGRU–Transformer model and validation-derived thresholds on 15,931 Ningbo ECGs without retraining, recalibration, or external threshold adjustment. We also examined calibration, demographic subgroups, computational efficiency, and complementary ECG-domain attribution methods. Results: Macro-AUROC reached 90.83% on PTB-XL fold 10 and 88.85% on Ningbo, indicating generally consistent diagnostic ranking with a modest reduction during external evaluation. Sensitivity analysis showed that CNN-only outperformed the frozen primary model on five of six endpoints. Attribution analyses highlighted qualitatively plausible lead and temporal patterns in representative examples, while calibration and subgroup analyses further characterized model behavior under dataset shift. Conclusions: Our framework integrates leakage-aware development, threshold-controlled testing, frozen external validation, and multimethod interpretability. These findings support its further prospective, locally calibrated evaluation as a potential aid for multi-label ECG interpretation.

This study examines the utilization of artificial intelligence (AI) and neural networking approaches for modeling the outbreak of the zoonotic Zika virus transmissions dynamics among hums and mosquitoes by applying the fractal–fractional operators. The theoretical and computational aspects enhance the significance of the study for control and treatment procedures. The theoretical aspect of the paper includes the existence, stability, and uniqueness results which validate the accuracy of the model and leading us to the computational results. The computational results are carried out with a computational scheme which gives us intricate data for the future predictions. The AI is used to validate, train, and test the data driven from the numerical simulations and measure the errors. Our work open doors for the researchers for the applications of the AI tools for the deep analysis of the zoonotic intricate disease dynamics.

Solving nonlinear differential equations reliably and effectively is critical for applications in epidemiology, ecology, and finance. Traditional finite difference approaches frequently exhibit numerical instability and fail to maintain crucial features such as positivity and boundedness. Motivated by these restrictions, we provide an unconditionally stable Nonstandard Finite Difference Predictor-Corrector (NSFD-PC) strategy for solving nonlinear quadratic Riccati differential equations. Particularly when the step size h rises, NSFD-PC typically yields the solution that is closer to the exact solution. Riccati differential equation is 1st-order quadratic ode with several systems and control theory applications. Our technique achieves first-order temporal precision while keeping the continuous model's fundamental qualitative properties. We compare the proposed NSFD-PC scheme to the usual Euler Predictor-Corrector approach and find that it performs much better in accuracy and consistency with exact answers. The results show that our method is a superior and dependable option for solving nonlinear differential equations, making it extremely useful in various applications.

On the one hand, convex functions are important to derive rigorous convergence rates, and on the other, synchronous functions are significant to solve statistical problems using Chebyshev inequalities. Therefore, fractional integral inequalities involving such functions play a crucial role in creating new models and methods. Although a large class of fractional operators have been used to establish inequalities, nevertheless, these operators having the Fox-H and the Meijer-G functions in their kernel have been applied to establish fractional integral inequalities for such important classes of functions. Taking motivation from these facts, the primary objective of this work is to develop fractional inequalities involving the Fox-H function for convex and synchronous functions. Since the Fox-H function generalizes several important special functions of fractional calculus, our results are significant to innovate the existing literature. The inventive features of these functions compel researchers to formulate deeper results involving them. Therefore, compared with the ongoing research in this field, our results are general enough to yield novel and inventive fractional inequalities. For instance, new inequalities involving the Meijer-G function are obtained as the special cases of these outcomes, and certain generalizations of Chebyshev inequality are also included in this article.